155 citations · 416 across the 26 of their papers we have counts for
6 papers · 1 filter
Generative Job Recommendations with Large Language Model
Zhi Zheng, Zhaopeng Qiu, Xiao Hu +3
The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process. Predominantly, current job recomme…
Preference or Intent? Double Disentangled Collaborative Filtering
Chao Wang, Hengshu Zhu, Dazhong Shen +2
People usually have different intents for choosing items, while their preferences under the same intent may also different. In traditional collaborative filtering approaches, both…
A Survey on Large Language Models for Recommendation
Likang Wu, Zhi Zheng, Zhaopeng Qiu +9
Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recomm…
A Survey on Knowledge Graph-Based Recommender Systems
Qingyu Guo, Fuzhen Zhuang, Chuan Qin +4
To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although…
SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback
Chao Wang, Hengshu Zhu, Chen Zhu +2
The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases. Different from explicit ratings…
Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning
Chen Zhu, Hengshu Zhu, Hui Xiong +4
Person-Job Fit is the process of matching the right talent for the right job by identifying talent competencies that are required for the job. While many qualitative efforts have b…